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Network Madness
Caroline Haythornthwaite,The iSchool @ The University of British Columbia
Presented at the Learning Analytics Summer Institute, 2014, Boston, MA
A node, a relation, a network
Social Network Perspective
—  Actors people, groups, organizations
—  Tied by one or more relations
◦  Sometimes strongly tied
◦  Sometimes weakly tied
—  Revealed as networks
—  Analyzed and displayed as graphs
Network Questions
—  Who learns from whom?
◦  Who talks to, gives help to,
collaborates with whom?
—  What do they learn from each
other?
—  Which media support which
kinds of learning?
—  What outcomes do
these relations build?
◦  Access to resources
◦  Trust, mobility, equity, etc.
—  What benefit accrues to the
network?
◦  social capital, shared
knowledge, shared resources
—  How do resources flow in the
network
abc123@321efg
abc123@321efg
abc123@321efg
Twitter – node size = accounts that are frequently mentioned,
replied to or whose tweets are frequently retweeted
abc123@321efg
abc123@321efg
abc123@321efg
Strong and Weak Ties
StrongTies …
—  Maintain more relations
—  Have more frequent
interaction
—  Include intimacy and self-disclosure
—  Use more media
—  Have higher reciprocity in
exchanges
Source of
• Freely given resources
• Feel obligation to share
! Questions
•  How do you build strong learning ties,
online and through computer media?
•  How do you motivate sharing in crowd-
and community-based initiatives?
•  How do you build learning
communities?
Strong and Weak Ties
WeakTies …
—  Engage in fewer, less intimate
exchanges
—  Have more instrumental
exchanges
—  Share fewer types of
information and support
—  Use fewer media
Source of…
• New information, new resources
• Have little or no obligation to share
à Questions
•  How do you bring peripheral actors
into the learning community?
•  What is the right mix of tie strength to
sustain innovation and commitment?
Social Networks and Learning
Who to whom
—  Who talks to, learns from, collaborates with
whom?
—  What are the attributes of these actors?
What
—  What do pairs talk about, do together?
—  What does the network talk about, do
together?
Structure
—  How does information circulate in a network?
—  Who are the key actors who facilitate or
hinder information movement?
—  Where is ‘expertise’ located?
Outcomes
—  What identifiable relations, actor interactions,
information exchange binds the network?
—  What social outcomes to these relations
build? trust, resources/services, mobility,
equality, opportunity, common knowledge
—  What benefit resides in the network? -- social
capital
—  Who talks to whom, about what, and via
which media?
—  Who learns from whom?
—  What relations constitute a learning tie? And/
or sustain a learning network?
—  Which media support which kinds of
ties and relations
◦  How are ties, relations, networks maintained,
online and off, in the service of learning?
—  What network structures emerge in the
service of learning?
—  What impact do different strategies,
pedagogies, teaching and learning
practices have on network relations, ties
and structures?
◦  How do emergent structures align with
pedagogical, collaborative, cooperative – or even
isolationist – expectations and intentions?
◦  Whate learning outcomes result for individuals,
cliques, networks?
—  What can we learn from network analyses
that inform design and design practice for
learning
Networks Are More Than Pictures
Networks show
—  density
—  actor centrality
—  centralization
—  cliques
—  stars
—  brokers
—  isolates
—  cliques
—  structural holes
—  path lengths
Network outcomes
—  Resource flow
◦  inclusion and
exclusion
◦  early and late
access to
information
—  Roles
◦  stars, gatekeepers,
entrepreneurs,
brokers,
translators
◦  information
suppliers, help
givers, social
support givers
—  Social structures
◦  Social capital,
resilience
Collaborating on
class work
Who learns from whom, about what,
and via what means?
—  Roles and Positions
◦  Technological guru, learner-
leaders, translators,
◦  Question askers and answerers
◦  Network stars and brokers
—  Relations
◦  Information exchange, social
support, help giving
—  Media
◦  Public and private
◦  Threaded (twitter) or
composite (wiki),
◦  Single (lecture hall) or multiple
(online/offline in various forms)
—  Structures
◦  What structures emerge in the
in open learning environments?
◦  What is a ‘good’ structure?
◦  What impact do different
strategies, pedagogies, teaching
and learning practices have on
network relations, ties and
structures?
—  Social Capital
◦  What benefits accrue to the
network?
—  Design
◦  What can we learn from
network analyses that inform
design and design practice for
learning?
Structure Tells Tales …
1
10
11
12
13
14
15
16
17
18
19
2
20
21
22
23
24
25
26
27
28
29
3
30
31
32
33
34
35
36
37
38
39
4
40
41
42
43
44
45
46
47
48
49
5
50
51
52
6
7
8
9
1
10
11
12
13
14
2
3
38
4
47
5
6
7
8
9
Network Evolution:
Email network over time Hidden Structures: External links; Internal core
Media Use: Chat, Discussion Board, Email
Media multiplexity
Classes and media form
latent tie structures on
which weak ties can build
into stronger ties
Discovery
—  Who
◦  How do we identify
actors and their roles in
learning networks
—  What
◦  What relations and ties
do people maintain? What
do they learn from each
other?
—  Structure
◦  What network
connections are revealed
through learning ties?
—  Moving toward
automated discovery
Node and tie discovery
Previous post is by Gabriel, Sam replies:
‘Nick,Ann, Gina, Gabriel:
I apologize for not backing this up with a good source,
but I know from reading about this topic that libraries…’	
  
Previous posts by Gabriel, Sam, Gina, and Eva, then:
‘Gina, I owe you a cookie.This is exactly what I wanted to know.
I was already planning on taking 302 next semester,
and now I have something to look forward to!’	
  
Post by Fred:
‘I wonder if that could be why other libraries
around the world have resisted changing –
it's too much work, and as Dan pointed out, too expensive.’	
  
Ex.1	
  
Ex.2	
  
Ex.3	
  
Gruzd,A. & Haythornthwaite, C. (2008). Automated discovery and analysis of social networks from threaded discussions. International
Sunbelt Social Network conference, Jan. 22-27, St. Pete’s Beach, Florida. [http://hdl.handle.net/2142/11528]
Nodes and ties in Twitter
—  Who mentions
and/or replies to
whom
—  Reveals a single
large component
with a moderate
periphery of
observers
Automated data collection: Who mentioned or
replied to whom, twitter network. Health care
learning community, #hcsmca (H&G, 2013)
Prestige and Influence
Green = social media health content providers
Blue = communicators, health related
Pink = advocacy
•  Who is mentioned,
replied to most has
the greatest prestige
(In-degree) = node size
here
•  Or, can see who
mentions or replies
to others most = the
greatest influence
(out-degree]
What do people learn from each other?
—  Learning Relations
◦  What did you learn from the 5-8
others with whom you
communicate most frequently?
◦  Questionnaires and content
analyses
0
10
20
30
40
50
60
70
Fact/Field
Process
M
ethod
R
esearch
Technology
G
enerate
Socialization
N
etw
orkingA
dm
inistration
Types of Learning: ReceivedInterdisciplinary Teams
Science Teachers
Distribution*of*‘learn*from’*relations*
Relation) 256) 100%)
Teaching*techniques*(T)* 173* 68*
Science*Content*(C)* 72* 28*
Classroom*Management*(M)* 32* 13*
External*Matters*(E)* 27* 11*
Administrative*functions*(A)* 17* 7*
None* 9* 4*
Relational multiplexity
in learning ties
Entrepreneurial Leadership in STEM : http://enlist.illinois.edu/
NB: caveat about data coverage: dataset covers only a limited number of schools and respondents, and data
collection from first time participants occurred at two time periods a year apart (one cohort in summer 2009,
two in summer 2010)
Revealing structures
Connections
across schools
build by learning
relationships:
I learn from / they
learn from me
about science
teaching
Learning from Networks
Using networks to interpret, analyze and design for community
A professional
development
network for a
school
(de Laat, 2010)
Shown back to
participants so they
can see how their
networks are
connected
More …
Look at
change
over
time
See how each medium
plays a role in
maintaining the
community: chat,
discussion,
email
Take a network
perspective on
motivating
contribution in
crowds and
communities
Explore these SN tools for analysis
of learning environments:
Netlytic https://netlytic.org/
(Anatoliy Gruzd)
SNAPP http://www.snappvis.org
(Shane Dawson)
Your Questions and
Network Studies Go Here
Further reading: short list -- see also http://haythorn.wordpress.com/
— Gruzd, A. & Haythornthwaite, C. (2013). Enabling community through social
media. Journal of Medical Internet Research. 2013;15(10):e248.
http://www.jmir.org/2013/10/e248/.
— Haythornthwaite, C. & De Laat, M. (2011). Social network informed design
for learning with educational technology. In A.D. Olofsson & J. O. Lindberg,
(Eds.). Informed Design of EducationalTechnologies in Higher Education (pp.
352-374). IGI Global.
— Haythornthwaite, C. (2008). Learning relations and networks in web-based
communities. International Journal ofWeb Based Communities, 4(2), 140-158.
http://www.inderscience.com/info/filter.php?aid=17669.
— Haythornthwaite, C. (2007). Social networks and online community. In A.
Joinson, K. McKenna, U. Reips & T. Postmes (Eds.), Oxford Handbook of Internet
Psychology (pp. 121-136). Oxford, UK: Oxford University Press.
Learning networks, learning analytics
—  Gruzd, A. & Haythornthwaite, C. (2013). Enabling community through social media. Journal of Medical Internet Research.
2013;15(10):e248. http://www.jmir.org/2013/10/e248/.
—  Haythornthwaite, C., De Laat, M. & Dawson, S. (Eds.) (2013). Learning analytics. American Behavioral Scientist, 57(10), whole issue.
—  Haythornthwaite, C. & De Laat, M. (2011). Social network informed design for learning with educational technology. In A.D.
Olofsson & J. O. Lindberg, (Eds.). Informed Design of EducationalTechnologies in Higher Education (pp. 352-374). IGI Global.
—  Haythornthwaite, C. (2008). Learning relations and networks in web-based communities. International Journal ofWeb Based
Communities, 4(2), 140-158. Selected as one of top 10 papers in IJWBC in its first 10 years and made open access: http://
www.inderscience.com/info/filter.php?aid=17669.
Discovering relations
—  Haythornthwaite, C., Gao,W. & Abd-El-Khalick, F. (2014). Networks of change: Learning from peers about science teaching.
Proceedings of the 47th Hawaii International Conference on System Sciences, Big Island, HI. Los Alamitos, CA: IEEE.
—  Haythornthwaite, C. (2006). Learning and knowledge exchanges in interdisciplinary collaborations. Journal of the American Society
for Information Science andTechnology, 57(8), 1079-1092.
—  Haythornthwaite, C. (2001). Exploring multiplexity: Social network structures in a computer-supported distance learning class.
The Information Society, 17(3), 211-226.
Structures: latent ties, internet connectivity, crowds and community
—  Budhathoki, N. & Haythornthwaite, C. (2013). Motivation for open collaboration: Crowd and community models and the case
of OpenStreetMap. American Behavioral Scientist, 57(5), 548 - 575.
—  Haythornthwaite, C. (Jan. 2009). Crowds and communities: Light and heavyweight models of peer production. Proceedings of the
42nd Hawaii International Conference on System Sciences. Los Alamitos, CA: IEEE. [http://hdl.handle.net/2142/9457]
—  Haythornthwaite, C. (2007). Social networks and online community. In A. Joinson, K. McKenna, U. Reips & T. Postmes (Eds.),
Oxford Handbook of Internet Psychology (pp. 121-136). Oxford, UK: Oxford University Press.
—  Haythornthwaite,C.(2005). Social networks and Internet connectivity effects. Information, Communication & Society, 8(2),125-147.
—  Haythornthwaite, C. (2002). Strong, weak and latent ties and the impact of new media. The Information Society, 18(5), 385 – 401.
Further reading: long list -- see also http://haythorn.wordpress.com/

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Hay network madness lasi14.pptx

  • 1. Network Madness Caroline Haythornthwaite,The iSchool @ The University of British Columbia Presented at the Learning Analytics Summer Institute, 2014, Boston, MA A node, a relation, a network
  • 2. Social Network Perspective —  Actors people, groups, organizations —  Tied by one or more relations ◦  Sometimes strongly tied ◦  Sometimes weakly tied —  Revealed as networks —  Analyzed and displayed as graphs
  • 3. Network Questions —  Who learns from whom? ◦  Who talks to, gives help to, collaborates with whom? —  What do they learn from each other? —  Which media support which kinds of learning? —  What outcomes do these relations build? ◦  Access to resources ◦  Trust, mobility, equity, etc. —  What benefit accrues to the network? ◦  social capital, shared knowledge, shared resources —  How do resources flow in the network abc123@321efg abc123@321efg abc123@321efg Twitter – node size = accounts that are frequently mentioned, replied to or whose tweets are frequently retweeted abc123@321efg abc123@321efg abc123@321efg
  • 4. Strong and Weak Ties StrongTies … —  Maintain more relations —  Have more frequent interaction —  Include intimacy and self-disclosure —  Use more media —  Have higher reciprocity in exchanges Source of • Freely given resources • Feel obligation to share ! Questions •  How do you build strong learning ties, online and through computer media? •  How do you motivate sharing in crowd- and community-based initiatives? •  How do you build learning communities?
  • 5. Strong and Weak Ties WeakTies … —  Engage in fewer, less intimate exchanges —  Have more instrumental exchanges —  Share fewer types of information and support —  Use fewer media Source of… • New information, new resources • Have little or no obligation to share à Questions •  How do you bring peripheral actors into the learning community? •  What is the right mix of tie strength to sustain innovation and commitment?
  • 6. Social Networks and Learning Who to whom —  Who talks to, learns from, collaborates with whom? —  What are the attributes of these actors? What —  What do pairs talk about, do together? —  What does the network talk about, do together? Structure —  How does information circulate in a network? —  Who are the key actors who facilitate or hinder information movement? —  Where is ‘expertise’ located? Outcomes —  What identifiable relations, actor interactions, information exchange binds the network? —  What social outcomes to these relations build? trust, resources/services, mobility, equality, opportunity, common knowledge —  What benefit resides in the network? -- social capital —  Who talks to whom, about what, and via which media? —  Who learns from whom? —  What relations constitute a learning tie? And/ or sustain a learning network? —  Which media support which kinds of ties and relations ◦  How are ties, relations, networks maintained, online and off, in the service of learning? —  What network structures emerge in the service of learning? —  What impact do different strategies, pedagogies, teaching and learning practices have on network relations, ties and structures? ◦  How do emergent structures align with pedagogical, collaborative, cooperative – or even isolationist – expectations and intentions? ◦  Whate learning outcomes result for individuals, cliques, networks? —  What can we learn from network analyses that inform design and design practice for learning
  • 7. Networks Are More Than Pictures Networks show —  density —  actor centrality —  centralization —  cliques —  stars —  brokers —  isolates —  cliques —  structural holes —  path lengths Network outcomes —  Resource flow ◦  inclusion and exclusion ◦  early and late access to information —  Roles ◦  stars, gatekeepers, entrepreneurs, brokers, translators ◦  information suppliers, help givers, social support givers —  Social structures ◦  Social capital, resilience Collaborating on class work
  • 8. Who learns from whom, about what, and via what means? —  Roles and Positions ◦  Technological guru, learner- leaders, translators, ◦  Question askers and answerers ◦  Network stars and brokers —  Relations ◦  Information exchange, social support, help giving —  Media ◦  Public and private ◦  Threaded (twitter) or composite (wiki), ◦  Single (lecture hall) or multiple (online/offline in various forms) —  Structures ◦  What structures emerge in the in open learning environments? ◦  What is a ‘good’ structure? ◦  What impact do different strategies, pedagogies, teaching and learning practices have on network relations, ties and structures? —  Social Capital ◦  What benefits accrue to the network? —  Design ◦  What can we learn from network analyses that inform design and design practice for learning?
  • 9. Structure Tells Tales … 1 10 11 12 13 14 15 16 17 18 19 2 20 21 22 23 24 25 26 27 28 29 3 30 31 32 33 34 35 36 37 38 39 4 40 41 42 43 44 45 46 47 48 49 5 50 51 52 6 7 8 9 1 10 11 12 13 14 2 3 38 4 47 5 6 7 8 9 Network Evolution: Email network over time Hidden Structures: External links; Internal core Media Use: Chat, Discussion Board, Email Media multiplexity Classes and media form latent tie structures on which weak ties can build into stronger ties
  • 10. Discovery —  Who ◦  How do we identify actors and their roles in learning networks —  What ◦  What relations and ties do people maintain? What do they learn from each other? —  Structure ◦  What network connections are revealed through learning ties? —  Moving toward automated discovery
  • 11. Node and tie discovery Previous post is by Gabriel, Sam replies: ‘Nick,Ann, Gina, Gabriel: I apologize for not backing this up with a good source, but I know from reading about this topic that libraries…’   Previous posts by Gabriel, Sam, Gina, and Eva, then: ‘Gina, I owe you a cookie.This is exactly what I wanted to know. I was already planning on taking 302 next semester, and now I have something to look forward to!’   Post by Fred: ‘I wonder if that could be why other libraries around the world have resisted changing – it's too much work, and as Dan pointed out, too expensive.’   Ex.1   Ex.2   Ex.3   Gruzd,A. & Haythornthwaite, C. (2008). Automated discovery and analysis of social networks from threaded discussions. International Sunbelt Social Network conference, Jan. 22-27, St. Pete’s Beach, Florida. [http://hdl.handle.net/2142/11528]
  • 12. Nodes and ties in Twitter —  Who mentions and/or replies to whom —  Reveals a single large component with a moderate periphery of observers Automated data collection: Who mentioned or replied to whom, twitter network. Health care learning community, #hcsmca (H&G, 2013)
  • 13. Prestige and Influence Green = social media health content providers Blue = communicators, health related Pink = advocacy •  Who is mentioned, replied to most has the greatest prestige (In-degree) = node size here •  Or, can see who mentions or replies to others most = the greatest influence (out-degree]
  • 14. What do people learn from each other? —  Learning Relations ◦  What did you learn from the 5-8 others with whom you communicate most frequently? ◦  Questionnaires and content analyses 0 10 20 30 40 50 60 70 Fact/Field Process M ethod R esearch Technology G enerate Socialization N etw orkingA dm inistration Types of Learning: ReceivedInterdisciplinary Teams Science Teachers Distribution*of*‘learn*from’*relations* Relation) 256) 100%) Teaching*techniques*(T)* 173* 68* Science*Content*(C)* 72* 28* Classroom*Management*(M)* 32* 13* External*Matters*(E)* 27* 11* Administrative*functions*(A)* 17* 7* None* 9* 4* Relational multiplexity in learning ties
  • 15. Entrepreneurial Leadership in STEM : http://enlist.illinois.edu/ NB: caveat about data coverage: dataset covers only a limited number of schools and respondents, and data collection from first time participants occurred at two time periods a year apart (one cohort in summer 2009, two in summer 2010) Revealing structures Connections across schools build by learning relationships: I learn from / they learn from me about science teaching
  • 16. Learning from Networks Using networks to interpret, analyze and design for community A professional development network for a school (de Laat, 2010) Shown back to participants so they can see how their networks are connected
  • 17. More … Look at change over time See how each medium plays a role in maintaining the community: chat, discussion, email Take a network perspective on motivating contribution in crowds and communities Explore these SN tools for analysis of learning environments: Netlytic https://netlytic.org/ (Anatoliy Gruzd) SNAPP http://www.snappvis.org (Shane Dawson) Your Questions and Network Studies Go Here
  • 18. Further reading: short list -- see also http://haythorn.wordpress.com/ — Gruzd, A. & Haythornthwaite, C. (2013). Enabling community through social media. Journal of Medical Internet Research. 2013;15(10):e248. http://www.jmir.org/2013/10/e248/. — Haythornthwaite, C. & De Laat, M. (2011). Social network informed design for learning with educational technology. In A.D. Olofsson & J. O. Lindberg, (Eds.). Informed Design of EducationalTechnologies in Higher Education (pp. 352-374). IGI Global. — Haythornthwaite, C. (2008). Learning relations and networks in web-based communities. International Journal ofWeb Based Communities, 4(2), 140-158. http://www.inderscience.com/info/filter.php?aid=17669. — Haythornthwaite, C. (2007). Social networks and online community. In A. Joinson, K. McKenna, U. Reips & T. Postmes (Eds.), Oxford Handbook of Internet Psychology (pp. 121-136). Oxford, UK: Oxford University Press.
  • 19. Learning networks, learning analytics —  Gruzd, A. & Haythornthwaite, C. (2013). Enabling community through social media. Journal of Medical Internet Research. 2013;15(10):e248. http://www.jmir.org/2013/10/e248/. —  Haythornthwaite, C., De Laat, M. & Dawson, S. (Eds.) (2013). Learning analytics. American Behavioral Scientist, 57(10), whole issue. —  Haythornthwaite, C. & De Laat, M. (2011). Social network informed design for learning with educational technology. In A.D. Olofsson & J. O. Lindberg, (Eds.). Informed Design of EducationalTechnologies in Higher Education (pp. 352-374). IGI Global. —  Haythornthwaite, C. (2008). Learning relations and networks in web-based communities. International Journal ofWeb Based Communities, 4(2), 140-158. Selected as one of top 10 papers in IJWBC in its first 10 years and made open access: http:// www.inderscience.com/info/filter.php?aid=17669. Discovering relations —  Haythornthwaite, C., Gao,W. & Abd-El-Khalick, F. (2014). Networks of change: Learning from peers about science teaching. Proceedings of the 47th Hawaii International Conference on System Sciences, Big Island, HI. Los Alamitos, CA: IEEE. —  Haythornthwaite, C. (2006). Learning and knowledge exchanges in interdisciplinary collaborations. Journal of the American Society for Information Science andTechnology, 57(8), 1079-1092. —  Haythornthwaite, C. (2001). Exploring multiplexity: Social network structures in a computer-supported distance learning class. The Information Society, 17(3), 211-226. Structures: latent ties, internet connectivity, crowds and community —  Budhathoki, N. & Haythornthwaite, C. (2013). Motivation for open collaboration: Crowd and community models and the case of OpenStreetMap. American Behavioral Scientist, 57(5), 548 - 575. —  Haythornthwaite, C. (Jan. 2009). Crowds and communities: Light and heavyweight models of peer production. Proceedings of the 42nd Hawaii International Conference on System Sciences. Los Alamitos, CA: IEEE. [http://hdl.handle.net/2142/9457] —  Haythornthwaite, C. (2007). Social networks and online community. In A. Joinson, K. McKenna, U. Reips & T. Postmes (Eds.), Oxford Handbook of Internet Psychology (pp. 121-136). Oxford, UK: Oxford University Press. —  Haythornthwaite,C.(2005). Social networks and Internet connectivity effects. Information, Communication & Society, 8(2),125-147. —  Haythornthwaite, C. (2002). Strong, weak and latent ties and the impact of new media. The Information Society, 18(5), 385 – 401. Further reading: long list -- see also http://haythorn.wordpress.com/